Summary: Explore and evaluate machine learning algorithms suitable for building a recommendation system. Identify, assess, and document the best methods for our use case.
Objectives:
Identify Algorithms: List ML algorithms for recommendations (e.g., collaborative filtering, matrix factorization).
Evaluate Suitability: Assess each algorithm's strengths, weaknesses, and data requirements.
Compare Performance: Review benchmarks or case studies if available.
Document Findings: Summarize recommendations and justifications.
Acceptance Criteria:
List of algorithms with evaluation and suitability analysis.
kvnguo
commented
1 month ago
Summary: Explore and evaluate machine learning algorithms suitable for building a recommendation system. Identify, assess, and document the best methods for our use case.
Objectives:
Identify Algorithms: List ML algorithms for recommendations (e.g., collaborative filtering, matrix factorization). Evaluate Suitability: Assess each algorithm's strengths, weaknesses, and data requirements. Compare Performance: Review benchmarks or case studies if available. Document Findings: Summarize recommendations and justifications.
Acceptance Criteria:
Assignees: TBD
Due Date: TBD